LeViT

Category: Hybrid · Code: timm/models/levit.py · Architectures: 20 · Pretrained weights: 10 (10 with ImageNet-1k results)

LeViT is a hybrid network for fast inference, a convolutional stem followed by attention stages that shrink the resolution with attention based downsampling and use attention biases for position.

Papers

• LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference

Usage

See the Quickstart for loading pretrained weights, image preprocessing and fine-tuning, and Feature Extraction for using the models as backbones.

>>> import timm
>>> model = timm.create_model('levit_256.fb_dist_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)

Model API support

✓ supported, ✓ (square) works for square sizes only, ✗ not supported, – not needed (the model already accepts any input size). Checked by running each feature on a randomly initialized model (Levit with levit_256d at 224, LevitDistilled with levit_128s at 224), other architectures of the same class are expected to behave the same. See the Model API page for details.

Architectures

GMACs and parameter counts are for the default input size and classifier of each architecture, from the benchmark results.

Pretrained weights

Top-1 is ImageNet-1k validation accuracy from results-imagenet.csv at the listed eval size, - if the weight has no ImageNet-1k result (e.g. pretraining or image-text weights). Classes is the size of the pretrained classifier, none for weights without one.

Citation

@article{graham2021levit,
  title={LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
  author={Benjamin Graham and Alaaeldin El-Nouby and Hugo Touvron and Pierre Stock and Armand Joulin and Herv'e J'egou and Matthijs Douze},
  journal={arXiv preprint arXiv:2104.01136},
  year={2021}
}